Systems and methods to control energy consumption efficiency

ABSTRACT

A controller is configured to exchange information with a building automation system and includes various executable programs for determining a real time operating efficiency, simulating a predicted or theoretical operating efficiency, comparing the same, and then adjusting one or more operating parameters on equipment utilized by a building&#39;s HVAC system. The controller operates to adjust an operating efficiency of the HVAC system. An adjustment module utilized by the controller may modify the HVAC equipment parameters based on the likelihood that various HVAC equipment operates in parallel and on-line near its natural operating curve. In addition, the adjustment module may include a self-learning aspect that permits the controller to more efficiently make similar, future adjustments as needed.

PRIORITY CLAIM

This application claims priority to U.S. Provisional Patent ApplicationNo. 61/110,353 filed on Oct. 31, 2008, the subject matter of which isincorporated herein in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to systems and methods forcontrolling energy consumption, and more specifically controlling energyconsumption of a heating, ventilation, and air conditioning (HVAC)system through a building automation system (BAS).

BACKGROUND OF THE INVENTION

Monitoring and controlling the energy consumption of a building, and inparticular the energy consumption of an HVAC system, has been achievedthrough a BAS having software executable algorithms that incorporatenumerical constant values corresponding to equipment operatingcharacteristics. The equipment of the HVAC system may include, but isnot limited to, chillers, pumps, condensers, filters, air conditioners,heaters, etc. The values utilized by the BAS are typically programmedduring installation of the HVAC system and set according to the localclimate and ambient conditions. These values may be changed periodicallyby manually evaluating and re-programming the BAS for anticipatedchanges in the local climate and ambient conditions.

Over time the local climate, ambient conditions and/or the operatingcharacteristics of the building and HVAC system may change. For example,the operating characteristics of the HVAC system may change if a pump isreplaced with a pump that has different characteristics. A more advancedtype of BAS may utilize optimization software for controlling the HVACsystem. This type of BAS will continue to adjust one or more operatingparameters such that each piece of equipment is operating at or near itsoptimum to satisfy the building cooling load with minimal total energyconsumption based on an equal marginal performance principle. However,the optimization software focuses on each piece of equipment such thatperiodically the numerical constants used in the optimization softwaremay still need to be manually adjusted. This is typically done byrecalculating the operating characteristics of the equipment, modifyingthe optimization software, restarting the BAS, observing the operationalefficiency of the HVAC system and iterating until the overall operatingefficiency of the HVAC system, as a whole, is within a desiredefficiency.

SUMMARY OF THE PARTICULAR EMBODIMENTS

A controller in communication with a building automation system (BAS)may be configured to automatically control an operating efficiency of anHVAC system. The controller utilizes real time operating data incomparison with predicted or theoretical information to automaticallyascertain and adjust the energy consumption of a building bycontemporaneously adjusting operating parameters for the HVAC equipment,validating the adjustments, and invoking a self-learning feature tominimize the time needed for similar adjustments in the future. By wayof example, the controller cooperates with the BAS to monitor the HVACsystem under prevailing energy demands with minimum energy wastage,thereby improving building energy management system efficiency.

In one aspect of the present invention, a controller for communicatingwith a BAS includes a communications interface operable to exchangeinformation contemporaneously in time between the controller and thebuilding automation system, the exchanged information carrying datacorresponding to operating parameters of equipment arranged in an HVACsystem; an optimization module having executable instructions fordetermining an operating efficiency of the HVAC system based on thepresent operating state of the equipment; a simulation module havingexecutable instructions for determining a predicted operating efficiencyof the HVAC system computed from installation specifications providedwith the HVAC equipment; a comparison module in data communication withthe optimization and simulation modules, the comparison moduleconfigured to determine whether the operating efficiency is below adesired threshold relative to the predicted operating efficiency; and anadjustment module in data communication with the comparison module, theadjustment module configured to transmit instructions to the buildingautomation system for changing at least one of the operating parametersfor at least one piece of equipment of the HVAC system, the adjustmentmodule further configured to process the instructions for changing in aself-learning aspect when the comparator, at a later time, determinesthe operating efficiency is below the desired threshold relative to thepredicted operating efficiency.

In another aspect of the present invention, a method for controlling anoperating efficiency of an HVAC system in communication with a buildingautomation system includes the steps of (1) exchanging informationcontemporaneously in time between a controller and the buildingautomation system, the exchanged information carrying data correspondingto operating parameters of equipment arranged in the HVAC system; (2)determining an operating efficiency of the HVAC system based on thepresent operating state of the equipment; (3) determining a predictedoperating efficiency of the HVAC system computed from installationspecifications provided with the HVAC equipment; (4) comparing whetherthe operating efficiency is below a desired threshold relative to thepredicted operating efficiency; (5) adjusting at least one of theoperating parameters for at least one piece of equipment of the HVACsystem; (6) transmitting the at least one adjustment to the buildingautomation system; and (7) triggering a self-learning feature of thecontroller for automatically recalling the at least one adjustment at alater time when the operating efficiency is again below the desiredthreshold.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative and alternative embodiments are described in detail belowwith reference to the following drawings.

FIG. 1 is a block diagram of a controller for communicating with abuilding automation system to control an operating efficiency of an HVACsystem according to an embodiment of the present invention;

FIG. 2 is another block diagram of the controller showing decisions madeby the controller to adjust equipment parameters of the HVAC system byway of the building automation system according to an embodiment of thepresent invention;

FIG. 3 is a block diagram illustrating a method for adjusting theequipment parameters of an HVAC system using the controller of FIG. 1according to an embodiment of the present invention; and

FIG. 4 is a graph of power efficiency curve for an HVAC system in abuilding controlled by the controller of FIG. 1.

DETAILED DESCRIPTION OF THE PARTICULAR EMBODIMENTS

In the following description, certain specific details are set forth inorder to provide a thorough understanding of various embodiments of theinvention. However, one skilled in the art will understand that theinvention may be practiced without these details. In other instances,well-known structures associated with HVAC systems and individual HVACcomponents, building climate or environmental control systems, buildingautomation systems and various processes, parameters, and operationthereof have not necessarily been shown or described in detail to avoidunnecessarily obscuring descriptions of the embodiments of theinvention. At least one embodiment of the invention includes aself-learning or self-correcting process in communication with the BASto receive selected input and then automatically tune or otherwiseoptimize one ore more aspects of a building's HVAC system.

Unless the context requires otherwise, throughout the specification andclaims which follow, the word “comprise” and variations thereof, suchas, “comprises” and “comprising” are to be construed in an open,inclusive sense, that is as “including, but not limited to.”

The energy performance of constant speed chillers, pumps and tower ismaximized when components are operated as closely as possible to fullload. Thus, chiller plant operating strategies generally involvesequencing plant equipment to minimize the amount of on-line equipment,which is operated at full load. In all-variable speed chiller plants,optimum performance is attained when the equipment is operating at aspecific part load that depends on the current external conditions. Thecurve of the loading at which each variable speed component achievesmaximum efficiency as the external conditions (pressure or temperature)vary is called the “natural curve” of that component. Optimum's systemsemploy a control methodology to sequence equipment so that plantoperation is at all times as close as possible to the natural curves ofthe equipment contained in the plant.

Chilled water distribution pumps are usually operated to maintain aspecific distribution differential pressure. Sometimes this pressuresetpoint is reset based on the maximum position of one or several of thevalves on the loads served by the system. However, the use of minimumpressure setpoints can contribute to substantial wasted pumping energy,especially during periods of low loads, which are frequent in systemsthat operate on continual daily schedule. The controller describedherein may provide distribution pump control that is particularlyeffective in large distribution circuits that may have differentcritical flow segments under different conditions or different times. Itshould be noted that HVAC components and/or equipment are operatedwithin their manufacturer's recommended limits. The controllercontinuously monitors the chilled and condenser water flows and rates offlow change, as well as the condensing water temperature, and limitsoperation to ensure these parameters remain within the range recommendedby the manufacturer for the specific equipment to which the control isapplied.

The controller is configured to exchange information with a BAS andincludes various executable programs for determining a real timeoperating efficiency, simulating a predicted or theoretical operatingefficiency, comparing the same, and then adjusting one or more operatingparameters on equipment utilized by a building's HVAC system. In oneembodiment the executable programs control variable speed loop coolingplants to establish a decrease in energy usage of a power utilizationsystem, for example, a building-wide HVAC system. In addition, thecontroller or at least one or more of the executable algorithms employedby the controller may comport with an equal marginal performanceprinciple for a particular system consistent with the teachings found inU.S. Pat. No. 6,185,946 to Thomas Hartman and an article authored by thesame entitled “Designing Efficient Systems with the Equal MarginalPerformance Principle,” ASHRAE Journal, Vol. 47, No. 7, July 2005,wherein both references are incorporated herein by reference in theirentireties. Hartman describes the numerical constants associated withthe operation of HVAC system equipment, such as, but not limited to,centrifugal pumps, fans, and variable speed drive centrifugal chillers.The numerical values are based on the likelihood that more HVACequipment operates in parallel and on-line near its natural operatingcurve.

In some embodiments, the BAS may communicate with an all-variable speedsystem to compensate for changes to equipment or operating conditionsautomatically, using self-correcting computer executable instructions.The controller may advantageously provide an automated technique toreplace the current manual tuning methods used to tune the HVAC system.In other embodiments, the controller automatically corrects theoperation of the BAS to compensate for changes in HVAC equipmentcharacteristics or external building load characteristics that may beattributed to the building and local climate.

In one embodiment, the controller is a self-learning controller in datacommunication with the BAS. The self-learning controller utilizes realtime energy usage surveillance, energy analysis, simulation, comparativeanalysis, and validation techniques to allow the controller toself-ascertain and self-adjust energy usage. The self-tuning featuresmay incorporate executable instructions that are processed within apower utilization system, for example a chilled water plant (CHW). Thecontroller receives data from the HVAC system for simulating theoperation thereof based on calculated system inputs for values liketotal system kilowatts used (TSkW) and a system cooling output tonnage(tons), which when combined provides a measure of energy efficiency. Thecontroller may also process data from the HVAC system to provide apredicted energy efficiency (kW/ton).

In one embodiment, a self-learning or self-tuning aspect of thecontroller may include a modification of the Hartman algorithms used tooptimize the HVAC system such that the control outputs are processed toenable the adaptive behavior of the HVAC by comparing the controloutputs with the simulated outputs to either modify (e.g., incrementallyadjust) the optimization algorithm values or to verify the real timeoperating efficiency against a simulated (e.g., predicted or theoretic)operating efficiency. The self-learning or self-tuning aspect of thecontroller may be referred to as a neural network process.

The neural-networking process includes executable instructions fordetecting hidden relationships in set patterns between the inputs,outputs, control and information points of the HVAC system. Theexecutable instructions provide for a “training phase” in which theoutput of a control parameter is compared with the desired output ordefined output as determined by a model based on the optimum or bestefficiency of power consumption. Model computational outputs indicatingerrors or other patterns that diminish optimum or best power consumptionefficiencies are propagated back or looped back toward the inputs of thecontrol model, applying and adjusting numerical or weighted values toreduce the computational errors, so that a prediction is acquired thatindicates the optimal power usage system pattern associated with optimumenergy consumption by the system.

The controller may include one or more processors dedicated to discreettasks, for example one processor may incorporate a “training phase”after adjustments to the HVAC system have been determined, anotherprocessor may compare real time data with predicted, theoretic orbest-case data, and yet another processor may evaluate an error in thesystem and suggest ways to reduce the error. In one embodiment, theself-tuning includes two phases, the “training phase” followed by the“verification phase”. During the first “training phase”, sample data(from the system model), containing both input and desired output isprocessed to optimize the actual control output, until the desiredenergy utilization efficiency is achieved. During the validation or“verification phase”, the error is no longer propagated back towards theinput parameters, but is used to predict or output energy related eventsor sequence values to serve as inputs for the various equipmentoperating in the building automation system.

If a large deviation between the actual control and system model occur,the system shall automatically change back to the “training phase”;until the system is optimized correctly then switch back to the“verification phase” again and remain there unless a deviation occursagain. This provides the self-learning controller that is adaptable tochanges in the HVAC system as well as adaptable to extreme environmentalchanges.

FIG. 1 shows an energy management system or controller 10 that includesa building automation system (BAS) 12 in data communication with an HVACsystem 14 and a controller 16, respectively. The data between the BAS 12and the HVAC system 14 may include, but is not limited to, energyrelated operational data 18 for the equipment of the HVAC system, wheresuch operational data 18 affect the overall energy efficiency of theHVAC system 14. The controller 18 includes executable instructions thatmay be arranged in different programs or modules and even processed byindependent processing means. In one embodiment, the controller 18includes an optimization module, a comparison module 22, a simulationmodule 24, and an adjustment (e.g., a self-learning and/or self-tuning)module 26. The controller 10 includes various modules, which may takethe form of executable instructions, programs, software, etc.), thatcooperatively interact to tune or self-adjust the operating parametersfor different types of HVAC equipment to account for changes in consumerdemand, inclement weather conditions, and other situations. By way ofexample, the controller 10 may interact to communicate to the BAS 12,which in turn communicates to the HVAC system 14, to adjust a flow rateof a pump, adjust a rotational speed of a fan, adjust a temperaturesetting of an air conditioning unit, etc.

The optimization module 20 may take the form of the executableinstructions taught by the Hartman references discussed above. By way ofexample, the optimization module 20 receives real time operational datato determine a real time operating efficiency 28 of the HVAC system 14based on the present operating state of the equipment of the HVAC system14. The simulation module 24 receives data 30, which may take the formof calculated input, for running simulation scenarios. By way ofexample, the simulation module 24 includes executable instructions fordetermining a simulated (e.g., predicted, theoretical or best case)operating efficiency 32 of the HVAC system 14 computed or calculatedfrom installation specification values that correspond to predeterminedoperational ranges for the individual pieces of equipment of the HVACsystem 14.

The respective efficiencies, 28, 32 are received by the comparisonmodule 22 for determining adjustment values 34 to be applied to one ormore pieces of equipment of the HVAC system 14. In one embodiment, thecomparison module 22 is in data communication with the optimizationmodule 20 and simulation module 24 and the comparison module 22 isconfigured to determine whether the real time operating efficiency 28 ofthe HVAC system 14 is below a desired threshold relative to thesimulated operating efficiency 32.

The adjustment values 34 are received by the adjustment module 26 todevelop or define new energy related sequence values 36 for improvingthe actual energy utilization efficiency HVAC system 14 and meets or atleast approaches the simulated operating efficiency 32. As discussedabove, the adjustment module 26 may include executable instructions forself-learning the adjustment values 34 and for self-tuning the BAS 12based on the sequence values 36. In one embodiment, the adjustmentmodule 26 operates as a neural network processing module that includescomputer readable media having instructions to execute various functionsusing either local or remote computer processing; whereas the remoteprocessing may, by way of example, be via a local network or theInternet. The adjustment values 34 may be further processed bymathematic multipliers, weighted and/or normalized.

FIG. 3 illustrates a method 100 for processing data from the BAS 12 withthe controller 16 (FIG. 1). At step 102, real time operating data aboutthe HVAC system is acquired from the BAS. The controller includes acommunications interface operable to exchange informationcontemporaneously in time between the controller and the BAS. Aspreviously stated, the exchanged information includes data correspondingto the real time operating parameters or characteristics of equipmentarranged in the HVAC system. At step 104, the real time operating datais received or input into an optimization module, such as theoptimization module described above. At step 106, individual equipmentefficiencies and an overall real time operating efficiency of the HVACsystem are computed based on executable instructions processed by theoptimization module and under prevailing equipment operating and weatherconditions.

Contemporaneously or simultaneously therewith at step 108, the data fordetermining a simulated (e.g., predicted, theoretical or best case)operating efficiency of the HVAC system is received by a simulationmodule. The data may take the form of installation specification valuesthat correspond to predetermined operational ranges for the individualpieces of HVAC equipment. At step 110, the simulation module determinesthe simulated operating efficiency of the HVAC system.

At step 112, a comparison module processes a decision gate to determinewhether the real time operating efficiency is within a desired thresholdof the simulated operating efficiency. If an affirmative (e.g., “yes” or“true”) answer is produced then this is communicated to the BAS and noadjustments are made. In a negative (e.g., “no” or “false”) answer isproduced then the real time operating data along with the installationspecification data of the HVAC system's equipment is passed along to anadjustment module. Thus, at step 114, adjustment values for one or morepieces of HVAC equipment are determined and a self-learning or trainingprocess is initiated or commenced.

At step 116, the adjustment values are tested or validated using avalidation module. In this step, the adjustment values or equipmentparameters are processed to determine if they achieve a desired overallHVAC system operating efficiency. Stated otherwise, do the changes tothe HVAC equipment actually produce an improvement in the overall systemenergy consumption and thus overall operating efficiency for the HVACsystem as communicated through the BAS. If the results are positive thenthe adjustment values are transmitted to the BAS for permanentimplementation into the HVAC system, until of course another adjustmentis needed due to weather or other changes.

FIG. 3 shows an embodiment of a process diagram 200 for the adjustmentmodule from FIG. 2. At step 202, the equipment adjustment values for theHVAC equipment are determined and set. At step 204, the adjustmentmodule communicates to provide instructions to the validation module totest or otherwise validate the adjustment values. At step 206, thecontroller determines whether the adjustments were validated. If not,then the adjustment values are re-set by the adjustment module toimprove the HVAC operating efficiency. If the values are validated, thenthese values are provided to the BAS for implementation thereof.

FIG. 4 shows a twenty-four hour power efficiency curve for the HVACsystem from data communicated through the BAS where the HVAC equipmenthas been continually controlled by an embodiment of the controller withthe executable instructions as described above. The HVAC system operateswith a fluctuating efficiency across the twenty-four period because thecontroller optimizes energy consumption by at least incrementallyadjusting one or more parameters of the HVAC equipment.

While the preferred embodiment of the invention has been illustrated anddescribed, as noted above, many changes can be made without departingfrom the spirit and scope of the invention. Accordingly, the scope ofthe invention is not limited by the disclosure of the preferredembodiment. Instead, the invention should be determined by reference tothe claims that follow.

1. A controller for communicating with a building automation system, thecontroller comprising: a communications interface operable to exchangeinformation contemporaneously in time between the controller and thebuilding automation system, the exchanged information carrying datacorresponding to operating parameters of equipment arranged in an HVACsystem; an optimization module having executable instructions fordetermining an operating efficiency of the HVAC system based on thepresent operating state of the equipment; a simulation module havingexecutable instructions for determining a predicted operating efficiencyof the HVAC system computed from installation specifications providedwith the HVAC equipment; a comparison module in data communication withthe optimization and simulation modules, the comparison moduleconfigured to determine whether the operating efficiency is below adesired threshold relative to the predicted operating efficiency; and anadjustment module in data communication with the comparison module, theadjustment module configured to transmit instructions to the buildingautomation system for changing at least one of the operating parametersfor at least one piece of equipment of the HVAC system, the adjustmentmodule further configured to process the instructions for changing in aself-learning aspect when the comparator, at a later time, determinesthe operating efficiency is below the desired threshold relative to thepredicted operating efficiency.
 2. The controller of claim 1, furthercomprising a validation module configured to determine whether theadjustment made to the at least one of the operating parameters for atleast one piece of equipment of the HVAC system results in a desiredoverall efficiency for the HVAC system as communicated to the buildingautomation system.
 3. The controller of claim 1, wherein the HVAC systemincludes a variable speed chiller cooling system.
 4. The controller ofclaim 1, wherein the predicted operating efficiency of the HVAC systemincludes information corresponding to a natural operating curve for theHVAC equipment.
 5. A method for controlling an operating efficiency ofan HVAC system in communication with a building automation system, themethod comprising: exchanging information contemporaneously in timebetween a controller and the building automation system, the exchangedinformation carrying data corresponding to operating parameters ofequipment arranged in the HVAC system; determining an operatingefficiency of the HVAC system based on the present operating state ofthe equipment; determining a predicted operating efficiency of the HVACsystem computed from installation specifications provided with the HVACequipment; comparing whether the operating efficiency is below a desiredthreshold relative to the predicted operating efficiency; adjusting atleast one of the operating parameters for at least one piece ofequipment of the HVAC system; transmitting the at least one adjustmentto the building automation system; and triggering a self-learningfeature of the controller for automatically recalling the at least oneadjustment at a later time when the operating efficiency is again belowthe desired threshold.
 6. The method of claim 5, further comprisingvalidating whether the adjustment made to the at least one of theoperating parameters for at least one piece of equipment of the HVACsystem results in a desired overall efficiency for the HVAC system ascommunicated to the building automation system.
 7. The method of claim5, wherein determining the operating efficiency of the HVAC systemincludes determining the operating efficiency of a variable speedchiller cooling system.